Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations604329
Missing cells0
Missing cells (%)0.0%
Duplicate rows646
Duplicate rows (%)0.1%
Total size in memory142.9 MiB
Average record size in memory248.0 B

Variable types

Numeric23
Categorical8

Alerts

P8 has constant value "0" Constant
V7 has constant value "0" Constant
V9 has constant value "0" Constant
Dataset has 646 (0.1%) duplicate rowsDuplicates
E1 is highly overall correlated with E2High correlation
E10 is highly overall correlated with E9High correlation
E2 is highly overall correlated with E1High correlation
E7 is highly overall correlated with E8 and 1 other fieldsHigh correlation
E8 is highly overall correlated with E7 and 1 other fieldsHigh correlation
E9 is highly overall correlated with E10 and 2 other fieldsHigh correlation
P3 is highly overall correlated with P4High correlation
P4 is highly overall correlated with P3High correlation
P6 is highly overall correlated with P7High correlation
P7 is highly overall correlated with P6High correlation
V1 is highly overall correlated with V10 and 2 other fieldsHigh correlation
V10 is highly overall correlated with V1High correlation
V6 is highly overall correlated with V1 and 1 other fieldsHigh correlation
V8 is highly overall correlated with V1 and 1 other fieldsHigh correlation
E3 is highly imbalanced (68.2%) Imbalance
V10 is highly imbalanced (53.6%) Imbalance
P5 is highly skewed (γ1 = 20.22503355) Skewed
P6 is highly skewed (γ1 = 89.78975173) Skewed
V11 is highly skewed (γ1 = 21.16556638) Skewed
E1 has 312394 (51.7%) zeros Zeros
E2 has 312372 (51.7%) zeros Zeros
E4 has 165662 (27.4%) zeros Zeros
E7 has 200662 (33.2%) zeros Zeros
E8 has 200498 (33.2%) zeros Zeros
E10 has 7050 (1.2%) zeros Zeros
E11 has 563098 (93.2%) zeros Zeros
V1 has 118217 (19.6%) zeros Zeros
V2 has 121025 (20.0%) zeros Zeros
V4 has 84180 (13.9%) zeros Zeros
V8 has 201832 (33.4%) zeros Zeros

Reproduction

Analysis started2024-12-14 10:44:07.730071
Analysis finished2024-12-14 10:48:16.204220
Duration4 minutes and 8.47 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

P1
Real number (ℝ)

Distinct113345
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.44902
Minimum-22.4812
Maximum101.351
Zeros0
Zeros (%)0.0%
Negative713
Negative (%)0.1%
Memory size4.6 MiB
2024-12-14T10:48:16.408572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-22.4812
5-th percentile27.471
Q131.7581
median34.1451
Q337.3119
95-th percentile48.13648
Maximum101.351
Range123.8322
Interquartile range (IQR)5.5538

Descriptive statistics

Standard deviation7.484629
Coefficient of variation (CV)0.21113783
Kurtosis17.767437
Mean35.44902
Median Absolute Deviation (MAD)2.6963
Skewness2.4459857
Sum21422871
Variance56.019671
MonotonicityNot monotonic
2024-12-14T10:48:17.837717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3769 37
 
< 0.1%
34.1163 35
 
< 0.1%
32.935 34
 
< 0.1%
33.8906 34
 
< 0.1%
34.0533 33
 
< 0.1%
33.0422 33
 
< 0.1%
33.2004 33
 
< 0.1%
33.5696 32
 
< 0.1%
33.8387 32
 
< 0.1%
33.099 32
 
< 0.1%
Other values (113335) 603994
99.9%
ValueCountFrequency (%)
-22.4812 1
< 0.1%
-22.476 1
< 0.1%
-22.4674 1
< 0.1%
-22.2871 1
< 0.1%
-22.2861 1
< 0.1%
-22.2656 1
< 0.1%
-22.2479 1
< 0.1%
-22.2303 1
< 0.1%
-22.2075 2
< 0.1%
-22.1805 1
< 0.1%
ValueCountFrequency (%)
101.351 1
 
< 0.1%
101.347 3
< 0.1%
101.344 1
 
< 0.1%
101.343 2
< 0.1%
101.342 1
 
< 0.1%
101.341 1
 
< 0.1%
101.34 1
 
< 0.1%
101.338 2
< 0.1%
101.336 3
< 0.1%
101.334 2
< 0.1%

P2
Real number (ℝ)

Distinct141467
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.996525
Minimum-45.6292
Maximum71.1737
Zeros0
Zeros (%)0.0%
Negative283
Negative (%)< 0.1%
Memory size4.6 MiB
2024-12-14T10:48:18.308858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-45.6292
5-th percentile6.51176
Q19.90354
median11.4004
Q313.6442
95-th percentile19.28622
Maximum71.1737
Range116.8029
Interquartile range (IQR)3.74066

Descriptive statistics

Standard deviation3.7602915
Coefficient of variation (CV)0.31344839
Kurtosis10.061429
Mean11.996525
Median Absolute Deviation (MAD)1.7655
Skewness0.86345039
Sum7249848.1
Variance14.139793
MonotonicityNot monotonic
2024-12-14T10:48:18.655771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8403 38
 
< 0.1%
10.8177 37
 
< 0.1%
11.1981 34
 
< 0.1%
10.9461 33
 
< 0.1%
10.5448 33
 
< 0.1%
10.5834 32
 
< 0.1%
10.6691 32
 
< 0.1%
11.454 32
 
< 0.1%
11.1566 32
 
< 0.1%
10.7 32
 
< 0.1%
Other values (141457) 603994
99.9%
ValueCountFrequency (%)
-45.6292 1
< 0.1%
-45.6285 1
< 0.1%
-45.626 1
< 0.1%
-45.6185 1
< 0.1%
-45.6148 1
< 0.1%
-45.6146 1
< 0.1%
-45.6114 1
< 0.1%
-45.6102 1
< 0.1%
-45.6076 1
< 0.1%
-45.6042 1
< 0.1%
ValueCountFrequency (%)
71.1737 1
< 0.1%
71.1702 1
< 0.1%
71.17 1
< 0.1%
71.1697 1
< 0.1%
71.1688 1
< 0.1%
71.1683 1
< 0.1%
71.1676 1
< 0.1%
71.1659 1
< 0.1%
71.1651 1
< 0.1%
71.1632 1
< 0.1%

P3
Real number (ℝ)

High correlation 

Distinct409
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1026.671
Minimum504
Maximum2512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:19.028459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum504
5-th percentile596
Q1792
median1000
Q31220
95-th percentile1592
Maximum2512
Range2008
Interquartile range (IQR)428

Descriptive statistics

Standard deviation309.27788
Coefficient of variation (CV)0.3012434
Kurtosis-0.27701979
Mean1026.671
Median Absolute Deviation (MAD)216
Skewness0.50798371
Sum6.2044708 × 108
Variance95652.805
MonotonicityNot monotonic
2024-12-14T10:48:19.442330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800 16737
 
2.8%
700 16032
 
2.7%
1100 15023
 
2.5%
1000 14927
 
2.5%
900 14265
 
2.4%
600 12074
 
2.0%
1200 11037
 
1.8%
1300 7939
 
1.3%
1400 6848
 
1.1%
1016 5590
 
0.9%
Other values (399) 483857
80.1%
ValueCountFrequency (%)
504 1331
0.2%
508 1767
0.3%
512 1136
0.2%
516 2259
0.4%
520 1276
0.2%
524 1284
0.2%
528 916
0.2%
532 1198
0.2%
536 763
 
0.1%
540 523
 
0.1%
ValueCountFrequency (%)
2512 14
 
< 0.1%
2416 18
< 0.1%
2408 24
< 0.1%
2272 14
 
< 0.1%
2252 14
 
< 0.1%
2240 16
 
< 0.1%
2192 12
 
< 0.1%
2180 20
< 0.1%
2172 20
< 0.1%
2168 44
< 0.1%

P4
Real number (ℝ)

High correlation 

Distinct409
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.061965
Minimum23.8853
Maximum119.048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:19.809775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum23.8853
5-th percentile37.6884
Q149.1803
median60
Q375.7576
95-th percentile100.671
Maximum119.048
Range95.1627
Interquartile range (IQR)26.5773

Descriptive statistics

Standard deviation19.75595
Coefficient of variation (CV)0.30838814
Kurtosis-0.29916705
Mean64.061965
Median Absolute Deviation (MAD)13.8462
Skewness0.643117
Sum38714503
Variance390.29757
MonotonicityNot monotonic
2024-12-14T10:48:20.166121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 16737
 
2.8%
85.7143 16032
 
2.7%
54.5455 15023
 
2.5%
60 14927
 
2.5%
66.6667 14265
 
2.4%
100 12074
 
2.0%
50 11037
 
1.8%
46.1538 7939
 
1.3%
42.8571 6848
 
1.1%
59.0551 5590
 
0.9%
Other values (399) 483857
80.1%
ValueCountFrequency (%)
23.8853 14
 
< 0.1%
24.8344 18
< 0.1%
24.9169 24
< 0.1%
26.4085 14
 
< 0.1%
26.643 14
 
< 0.1%
26.7857 16
 
< 0.1%
27.3723 12
 
< 0.1%
27.5229 20
< 0.1%
27.6243 20
< 0.1%
27.6753 44
< 0.1%
ValueCountFrequency (%)
119.048 1331
0.2%
118.11 1767
0.3%
117.188 1136
0.2%
116.279 2259
0.4%
115.385 1276
0.2%
114.504 1284
0.2%
113.636 916
0.2%
112.782 1198
0.2%
111.94 763
 
0.1%
111.111 523
 
0.1%

P5
Real number (ℝ)

Skewed 

Distinct1219
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17892299
Minimum0.03892
Maximum27.2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:20.534429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.03892
5-th percentile0.076542
Q10.09211
median0.105083
Q30.138814
95-th percentile0.310061
Maximum27.2022
Range27.16328
Interquartile range (IQR)0.046704

Descriptive statistics

Standard deviation0.37230859
Coefficient of variation (CV)2.0808315
Kurtosis937.06714
Mean0.17892299
Median Absolute Deviation (MAD)0.016865
Skewness20.225034
Sum108128.35
Variance0.13861369
MonotonicityNot monotonic
2024-12-14T10:48:20.912937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.096002 15742
 
2.6%
0.099894 15548
 
2.6%
0.097299 15308
 
2.5%
0.101191 15213
 
2.5%
0.093407 14936
 
2.5%
0.094705 14838
 
2.5%
0.102489 14572
 
2.4%
0.098597 14570
 
2.4%
0.09211 14295
 
2.4%
0.103786 14055
 
2.3%
Other values (1209) 455252
75.3%
ValueCountFrequency (%)
0.03892 12
 
< 0.1%
0.040217 12
 
< 0.1%
0.041514 6
 
< 0.1%
0.042812 28
 
< 0.1%
0.044109 6
 
< 0.1%
0.045406 16
 
< 0.1%
0.046704 18
 
< 0.1%
0.048001 102
< 0.1%
0.049298 50
< 0.1%
0.050596 78
< 0.1%
ValueCountFrequency (%)
27.2022 16
< 0.1%
18.6658 2
 
< 0.1%
18.0081 4
 
< 0.1%
13.8554 12
< 0.1%
11.72 18
< 0.1%
10.35 4
 
< 0.1%
9.72473 12
< 0.1%
8.3833 2
 
< 0.1%
7.51406 12
< 0.1%
7.45831 8
< 0.1%

P6
Real number (ℝ)

High correlation  Skewed 

Distinct419
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean845.38461
Minimum128
Maximum228812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:21.251714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile572
Q1668
median800
Q3900
95-th percentile1136
Maximum228812
Range228684
Interquartile range (IQR)232

Descriptive statistics

Standard deviation2505.3351
Coefficient of variation (CV)2.9635448
Kurtosis8164.6325
Mean845.38461
Median Absolute Deviation (MAD)120
Skewness89.789752
Sum5.1089044 × 108
Variance6276704.2
MonotonicityNot monotonic
2024-12-14T10:48:21.622283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
856 7222
 
1.2%
860 7198
 
1.2%
840 6977
 
1.2%
848 6910
 
1.1%
852 6748
 
1.1%
868 6633
 
1.1%
836 6614
 
1.1%
864 6611
 
1.1%
828 6517
 
1.1%
844 6449
 
1.1%
Other values (409) 536450
88.8%
ValueCountFrequency (%)
128 6
 
< 0.1%
136 6
 
< 0.1%
140 24
< 0.1%
144 13
< 0.1%
148 18
< 0.1%
156 2
 
< 0.1%
160 4
 
< 0.1%
164 4
 
< 0.1%
168 4
 
< 0.1%
184 8
 
< 0.1%
ValueCountFrequency (%)
228812 72
< 0.1%
20104 32
< 0.1%
7272 32
< 0.1%
6740 44
< 0.1%
6100 68
< 0.1%
5152 60
< 0.1%
4528 26
 
< 0.1%
4424 30
< 0.1%
4312 26
 
< 0.1%
3800 52
< 0.1%

P7
Real number (ℝ)

High correlation 

Distinct419
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.887628
Minimum0.262224
Maximum468.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:21.955307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.262224
5-th percentile52.8169
Q166.6667
median75
Q389.8204
95-th percentile104.895
Maximum468.75
Range468.48778
Interquartile range (IQR)23.1537

Descriptive statistics

Standard deviation18.57793
Coefficient of variation (CV)0.23852222
Kurtosis22.747124
Mean77.887628
Median Absolute Deviation (MAD)11.7052
Skewness1.9371239
Sum47069752
Variance345.13949
MonotonicityNot monotonic
2024-12-14T10:48:22.644381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.0935 7222
 
1.2%
69.7674 7198
 
1.2%
71.4286 6977
 
1.2%
70.7547 6910
 
1.1%
70.4225 6748
 
1.1%
69.1244 6633
 
1.1%
71.7703 6614
 
1.1%
69.4444 6611
 
1.1%
72.4638 6517
 
1.1%
71.09 6449
 
1.1%
Other values (409) 536450
88.8%
ValueCountFrequency (%)
0.262224 72
< 0.1%
2.98448 32
< 0.1%
8.25083 32
< 0.1%
8.90208 44
< 0.1%
9.83607 68
< 0.1%
11.646 60
< 0.1%
13.2509 26
 
< 0.1%
13.5624 30
< 0.1%
13.9147 26
 
< 0.1%
15.7895 52
< 0.1%
ValueCountFrequency (%)
468.75 6
 
< 0.1%
441.176 6
 
< 0.1%
428.571 24
< 0.1%
416.667 13
< 0.1%
405.405 18
< 0.1%
384.615 2
 
< 0.1%
375 4
 
< 0.1%
365.854 4
 
< 0.1%
357.143 4
 
< 0.1%
326.087 8
 
< 0.1%

P8
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0
604329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters604329
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 604329
100.0%

Length

2024-12-14T10:48:23.222050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T10:48:23.514614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 604329
100.0%

Most occurring characters

ValueCountFrequency (%)
0 604329
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

E1
Real number (ℝ)

High correlation  Zeros 

Distinct13640
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.512332
Minimum0
Maximum243.991
Zeros312394
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:23.841060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q328.24
95-th percentile32.357
Maximum243.991
Range243.991
Interquartile range (IQR)28.24

Descriptive statistics

Standard deviation14.049071
Coefficient of variation (CV)1.3364371
Kurtosis0.15364664
Mean10.512332
Median Absolute Deviation (MAD)0
Skewness0.76607487
Sum6352907.3
Variance197.3764
MonotonicityNot monotonic
2024-12-14T10:48:24.373202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 312394
51.7%
0.018 2317
 
0.4%
0.012 2102
 
0.3%
0.02 1935
 
0.3%
0.017 1818
 
0.3%
0.016 1707
 
0.3%
0.01 1569
 
0.3%
0.022 1548
 
0.3%
0.007 1536
 
0.3%
0.015 1491
 
0.2%
Other values (13630) 275912
45.7%
ValueCountFrequency (%)
0 312394
51.7%
0.001 202
 
< 0.1%
0.002 414
 
0.1%
0.003 742
 
0.1%
0.004 900
 
0.1%
0.005 845
 
0.1%
0.006 1238
 
0.2%
0.007 1536
 
0.3%
0.008 1154
 
0.2%
0.009 1095
 
0.2%
ValueCountFrequency (%)
243.991 6
 
< 0.1%
192.974 18
< 0.1%
96.839 20
< 0.1%
80.629 4
 
< 0.1%
69.791 2
 
< 0.1%
69.573 2
 
< 0.1%
59.845 6
 
< 0.1%
59.662 6
 
< 0.1%
48.487 2
 
< 0.1%
45.588 6
 
< 0.1%

E2
Real number (ℝ)

High correlation  Zeros 

Distinct34569
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.79004
Minimum0
Maximum359.995
Zeros312372
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:24.746517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3211.584
95-th percentile341.094
Maximum359.995
Range359.995
Interquartile range (IQR)211.584

Descriptive statistics

Standard deviation127.25863
Coefficient of variation (CV)1.2380443
Kurtosis-0.99908566
Mean102.79004
Median Absolute Deviation (MAD)0
Skewness0.75421952
Sum62119005
Variance16194.759
MonotonicityNot monotonic
2024-12-14T10:48:25.093145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 312372
51.7%
108.994 700
 
0.1%
317.933 461
 
0.1%
325.862 452
 
0.1%
279.613 440
 
0.1%
345.262 410
 
0.1%
354.205 398
 
0.1%
245.181 396
 
0.1%
222.11 365
 
0.1%
333.932 350
 
0.1%
Other values (34559) 287985
47.7%
ValueCountFrequency (%)
0 312372
51.7%
0.017 4
 
< 0.1%
0.042 14
 
< 0.1%
0.044 6
 
< 0.1%
0.057 4
 
< 0.1%
0.061 2
 
< 0.1%
0.073 4
 
< 0.1%
0.104 8
 
< 0.1%
0.114 4
 
< 0.1%
0.121 24
 
< 0.1%
ValueCountFrequency (%)
359.995 4
 
< 0.1%
359.958 8
 
< 0.1%
359.938 2
 
< 0.1%
359.89 32
< 0.1%
359.889 12
 
< 0.1%
359.867 6
 
< 0.1%
359.864 4
 
< 0.1%
359.862 4
 
< 0.1%
359.845 2
 
< 0.1%
359.82 4
 
< 0.1%

E3
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0
550538 
4
 
40602
1
 
13189

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters604329
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 550538
91.1%
4 40602
 
6.7%
1 13189
 
2.2%

Length

2024-12-14T10:48:25.421571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T10:48:25.652898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 550538
91.1%
4 40602
 
6.7%
1 13189
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 550538
91.1%
4 40602
 
6.7%
1 13189
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 550538
91.1%
4 40602
 
6.7%
1 13189
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 550538
91.1%
4 40602
 
6.7%
1 13189
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 550538
91.1%
4 40602
 
6.7%
1 13189
 
2.2%

E4
Real number (ℝ)

Zeros 

Distinct252
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.2301362
Minimum-250
Maximum260
Zeros165662
Zeros (%)27.4%
Negative231562
Negative (%)38.3%
Memory size4.6 MiB
2024-12-14T10:48:25.999884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-250
5-th percentile-50
Q1-8
median0
Q36
95-th percentile30
Maximum260
Range510
Interquartile range (IQR)14

Descriptive statistics

Standard deviation35.508596
Coefficient of variation (CV)-8.3941967
Kurtosis19.763359
Mean-4.2301362
Median Absolute Deviation (MAD)8
Skewness-2.4677709
Sum-2556394
Variance1260.8604
MonotonicityNot monotonic
2024-12-14T10:48:26.864398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 165662
27.4%
4 30784
 
5.1%
-4 29128
 
4.8%
8 24214
 
4.0%
-8 24090
 
4.0%
2 20774
 
3.4%
12 18271
 
3.0%
-12 17813
 
2.9%
-18 17129
 
2.8%
-2 17047
 
2.8%
Other values (242) 239417
39.6%
ValueCountFrequency (%)
-250 2781
0.5%
-248 92
 
< 0.1%
-246 24
 
< 0.1%
-244 53
 
< 0.1%
-242 28
 
< 0.1%
-240 20
 
< 0.1%
-238 74
 
< 0.1%
-236 88
 
< 0.1%
-234 28
 
< 0.1%
-232 10
 
< 0.1%
ValueCountFrequency (%)
260 4
 
< 0.1%
254 4
 
< 0.1%
250 126
< 0.1%
248 14
 
< 0.1%
246 18
 
< 0.1%
244 4
 
< 0.1%
242 8
 
< 0.1%
240 20
 
< 0.1%
238 48
 
< 0.1%
236 8
 
< 0.1%

E5
Real number (ℝ)

Distinct254
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016262423
Minimum0.008
Maximum0.023939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:27.392565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.012599
Q10.015686
median0.016001
Q30.016694
95-th percentile0.020789
Maximum0.023939
Range0.015939
Interquartile range (IQR)0.001008

Descriptive statistics

Standard deviation0.0023041242
Coefficient of variation (CV)0.14168395
Kurtosis3.8164764
Mean0.016262423
Median Absolute Deviation (MAD)0.000504
Skewness0.17510035
Sum9827.8538
Variance5.3089885 × 10-6
MonotonicityNot monotonic
2024-12-14T10:48:27.998915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015875 39648
 
6.6%
0.016001 39068
 
6.5%
0.016064 27713
 
4.6%
0.015812 22458
 
3.7%
0.016127 21882
 
3.6%
0.015938 20300
 
3.4%
0.015749 17979
 
3.0%
0.01619 16734
 
2.8%
0.016253 15769
 
2.6%
0.015686 14547
 
2.4%
Other values (244) 368231
60.9%
ValueCountFrequency (%)
0.008 4045
0.7%
0.008063 92
 
< 0.1%
0.008126 178
 
< 0.1%
0.008189 158
 
< 0.1%
0.008252 56
 
< 0.1%
0.008315 261
 
< 0.1%
0.008378 218
 
< 0.1%
0.008441 140
 
< 0.1%
0.008504 80
 
< 0.1%
0.008567 1027
 
0.2%
ValueCountFrequency (%)
0.023939 3832
0.6%
0.023876 168
 
< 0.1%
0.023813 110
 
< 0.1%
0.02375 182
 
< 0.1%
0.023687 160
 
< 0.1%
0.023624 110
 
< 0.1%
0.023561 148
 
< 0.1%
0.023498 162
 
< 0.1%
0.023435 126
 
< 0.1%
0.023372 274
 
< 0.1%

E6
Real number (ℝ)

Distinct247
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean358.67474
Minimum260
Maximum513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:28.567214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum260
5-th percentile308
Q1348
median365
Q3367
95-th percentile399
Maximum513
Range253
Interquartile range (IQR)19

Descriptive statistics

Standard deviation27.399973
Coefficient of variation (CV)0.076392256
Kurtosis2.9485458
Mean358.67474
Median Absolute Deviation (MAD)13
Skewness-0.46987203
Sum2.1675755 × 108
Variance750.7585
MonotonicityNot monotonic
2024-12-14T10:48:29.224512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
366 81211
 
13.4%
367 70791
 
11.7%
348 18309
 
3.0%
357 13166
 
2.2%
350 13111
 
2.2%
364 10680
 
1.8%
343 9492
 
1.6%
356 8433
 
1.4%
352 8323
 
1.4%
351 8087
 
1.3%
Other values (237) 362726
60.0%
ValueCountFrequency (%)
260 2927
0.5%
261 34
 
< 0.1%
262 8
 
< 0.1%
263 88
 
< 0.1%
264 14
 
< 0.1%
265 30
 
< 0.1%
266 1020
 
0.2%
267 698
 
0.1%
268 688
 
0.1%
269 522
 
0.1%
ValueCountFrequency (%)
513 182
< 0.1%
512 18
 
< 0.1%
511 12
 
< 0.1%
510 14
 
< 0.1%
509 12
 
< 0.1%
508 10
 
< 0.1%
507 10
 
< 0.1%
506 24
 
< 0.1%
505 6
 
< 0.1%
504 18
 
< 0.1%

E7
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7572961
Minimum0
Maximum25
Zeros200662
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:29.770752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile10
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8548523
Coefficient of variation (CV)1.624571
Kurtosis8.1349177
Mean1.7572961
Median Absolute Deviation (MAD)1
Skewness2.8441188
Sum1061985
Variance8.1501815
MonotonicityNot monotonic
2024-12-14T10:48:30.270561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 211515
35.0%
0 200662
33.2%
2 111262
18.4%
3 27018
 
4.5%
10 14403
 
2.4%
9 10993
 
1.8%
8 8342
 
1.4%
11 4050
 
0.7%
14 3206
 
0.5%
13 3039
 
0.5%
Other values (16) 9839
 
1.6%
ValueCountFrequency (%)
0 200662
33.2%
1 211515
35.0%
2 111262
18.4%
3 27018
 
4.5%
4 582
 
0.1%
5 1087
 
0.2%
6 550
 
0.1%
7 1612
 
0.3%
8 8342
 
1.4%
9 10993
 
1.8%
ValueCountFrequency (%)
25 26
 
< 0.1%
24 40
 
< 0.1%
23 6
 
< 0.1%
22 14
 
< 0.1%
21 22
 
< 0.1%
20 42
 
< 0.1%
19 46
 
< 0.1%
18 106
 
< 0.1%
17 374
0.1%
16 767
0.1%

E8
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3830579
Minimum0
Maximum9
Zeros200498
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:30.785551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6088069
Coefficient of variation (CV)1.1632245
Kurtosis6.3421758
Mean1.3830579
Median Absolute Deviation (MAD)1
Skewness2.2002175
Sum835822
Variance2.5882596
MonotonicityNot monotonic
2024-12-14T10:48:31.309641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 200498
33.2%
1 170396
28.2%
2 155712
25.8%
3 42886
 
7.1%
6 8932
 
1.5%
7 7329
 
1.2%
4 4713
 
0.8%
5 4653
 
0.8%
9 4652
 
0.8%
8 4558
 
0.8%
ValueCountFrequency (%)
0 200498
33.2%
1 170396
28.2%
2 155712
25.8%
3 42886
 
7.1%
4 4713
 
0.8%
5 4653
 
0.8%
6 8932
 
1.5%
7 7329
 
1.2%
8 4558
 
0.8%
9 4652
 
0.8%
ValueCountFrequency (%)
9 4652
 
0.8%
8 4558
 
0.8%
7 7329
 
1.2%
6 8932
 
1.5%
5 4653
 
0.8%
4 4713
 
0.8%
3 42886
 
7.1%
2 155712
25.8%
1 170396
28.2%
0 200498
33.2%

E9
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
1
529868 
0
74461 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters604329
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 529868
87.7%
0 74461
 
12.3%

Length

2024-12-14T10:48:31.684836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T10:48:31.905509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 529868
87.7%
0 74461
 
12.3%

Most occurring characters

ValueCountFrequency (%)
1 529868
87.7%
0 74461
 
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 529868
87.7%
0 74461
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 529868
87.7%
0 74461
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 529868
87.7%
0 74461
 
12.3%

E10
Real number (ℝ)

High correlation  Zeros 

Distinct123
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.311256
Minimum0
Maximum127
Zeros7050
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:32.194434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q152
median67
Q373
95-th percentile100
Maximum127
Range127
Interquartile range (IQR)21

Descriptive statistics

Standard deviation18.891029
Coefficient of variation (CV)0.29838342
Kurtosis1.1064062
Mean63.311256
Median Absolute Deviation (MAD)8
Skewness-0.43353981
Sum38260828
Variance356.87098
MonotonicityNot monotonic
2024-12-14T10:48:32.549589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 48103
 
8.0%
100 45110
 
7.5%
71 25570
 
4.2%
73 24239
 
4.0%
70 19818
 
3.3%
74 19322
 
3.2%
69 18318
 
3.0%
68 16399
 
2.7%
75 16283
 
2.7%
67 16173
 
2.7%
Other values (113) 354994
58.7%
ValueCountFrequency (%)
0 7050
1.2%
1 838
 
0.1%
2 324
 
0.1%
3 326
 
0.1%
4 192
 
< 0.1%
5 200
 
< 0.1%
6 326
 
0.1%
7 135
 
< 0.1%
8 206
 
< 0.1%
9 111
 
< 0.1%
ValueCountFrequency (%)
127 18
< 0.1%
126 34
< 0.1%
125 24
< 0.1%
124 22
< 0.1%
123 6
 
< 0.1%
122 10
 
< 0.1%
119 20
< 0.1%
117 34
< 0.1%
116 40
< 0.1%
115 14
 
< 0.1%

E11
Real number (ℝ)

Zeros 

Distinct120
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.315265
Minimum0
Maximum52.4
Zeros563098
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:32.883276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15.6
Maximum52.4
Range52.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.2472039
Coefficient of variation (CV)3.9894651
Kurtosis15.659874
Mean1.315265
Median Absolute Deviation (MAD)0
Skewness4.0628882
Sum794852.8
Variance27.533149
MonotonicityNot monotonic
2024-12-14T10:48:33.248660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 563098
93.2%
22 1520
 
0.3%
24.8 1337
 
0.2%
22.4 1184
 
0.2%
23.6 1100
 
0.2%
22.8 1096
 
0.2%
21.6 1067
 
0.2%
21.2 1058
 
0.2%
25.2 1027
 
0.2%
24.4 1019
 
0.2%
Other values (110) 30823
 
5.1%
ValueCountFrequency (%)
0 563098
93.2%
0.4 284
 
< 0.1%
0.8 203
 
< 0.1%
1.2 205
 
< 0.1%
1.6 191
 
< 0.1%
2 183
 
< 0.1%
2.4 211
 
< 0.1%
2.8 174
 
< 0.1%
3.2 150
 
< 0.1%
3.6 165
 
< 0.1%
ValueCountFrequency (%)
52.4 5
< 0.1%
52 10
< 0.1%
51.6 4
 
< 0.1%
51.2 4
 
< 0.1%
50.8 6
< 0.1%
50.4 2
 
< 0.1%
50 3
 
< 0.1%
49.6 2
 
< 0.1%
47.6 2
 
< 0.1%
46.4 2
 
< 0.1%

V1
Real number (ℝ)

High correlation  Zeros 

Distinct12418
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.965412
Minimum0
Maximum129.7
Zeros118217
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:33.577906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141.93
median100.4
Q3108.5
95-th percentile117.23
Maximum129.7
Range129.7
Interquartile range (IQR)66.57

Descriptive statistics

Standard deviation44.387031
Coefficient of variation (CV)0.576714
Kurtosis-0.85280395
Mean76.965412
Median Absolute Deviation (MAD)11.54
Skewness-0.94146962
Sum46512430
Variance1970.2085
MonotonicityNot monotonic
2024-12-14T10:48:33.949128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118217
 
19.6%
110.53 307
 
0.1%
107.01 305
 
0.1%
107.16 302
 
< 0.1%
109.24 298
 
< 0.1%
108.39 296
 
< 0.1%
108.93 295
 
< 0.1%
106.82 293
 
< 0.1%
108.18 290
 
< 0.1%
108.5 289
 
< 0.1%
Other values (12408) 483437
80.0%
ValueCountFrequency (%)
0 118217
19.6%
0.69 98
 
< 0.1%
0.7 26
 
< 0.1%
0.71 23
 
< 0.1%
0.72 20
 
< 0.1%
0.73 24
 
< 0.1%
0.74 21
 
< 0.1%
0.75 18
 
< 0.1%
0.76 44
 
< 0.1%
0.77 25
 
< 0.1%
ValueCountFrequency (%)
129.7 2
 
< 0.1%
129.49 2
 
< 0.1%
129.48 2
 
< 0.1%
129.4 2
 
< 0.1%
129.36 2
 
< 0.1%
129.35 6
< 0.1%
129.33 2
 
< 0.1%
129.19 2
 
< 0.1%
129.13 1
 
< 0.1%
129.12 1
 
< 0.1%

V2
Real number (ℝ)

Zeros 

Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.037709575
Minimum-4.795
Maximum3.99
Zeros121025
Zeros (%)20.0%
Negative259081
Negative (%)42.9%
Memory size4.6 MiB
2024-12-14T10:48:34.323981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-4.795
5-th percentile-0.56
Q1-0.175
median0
Q30.07
95-th percentile0.455
Maximum3.99
Range8.785
Interquartile range (IQR)0.245

Descriptive statistics

Standard deviation0.40389577
Coefficient of variation (CV)-10.710695
Kurtosis14.688013
Mean-0.037709575
Median Absolute Deviation (MAD)0.175
Skewness-0.75945195
Sum-22788.99
Variance0.16313179
MonotonicityNot monotonic
2024-12-14T10:48:34.646389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 121025
20.0%
-0.07 80064
13.2%
0.07 78730
13.0%
0.175 50521
8.4%
-0.175 41711
 
6.9%
-0.28 39517
 
6.5%
0.28 32683
 
5.4%
-0.385 30611
 
5.1%
-0.56 25585
 
4.2%
0.385 21031
 
3.5%
Other values (80) 82851
13.7%
ValueCountFrequency (%)
-4.795 1
 
< 0.1%
-4.69 2
 
< 0.1%
-4.48 5
 
< 0.1%
-4.375 2
 
< 0.1%
-4.305 11
 
< 0.1%
-4.2 11
 
< 0.1%
-4.095 5
 
< 0.1%
-3.99 11
 
< 0.1%
-3.885 23
< 0.1%
-3.815 28
< 0.1%
ValueCountFrequency (%)
3.99 1
 
< 0.1%
3.885 4
 
< 0.1%
3.815 2
 
< 0.1%
3.71 9
 
< 0.1%
3.605 16
< 0.1%
3.5 15
< 0.1%
3.395 11
 
< 0.1%
3.325 20
< 0.1%
3.22 33
< 0.1%
3.115 31
< 0.1%

V3
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean573.78643
Minimum240
Maximum1023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:34.933408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum240
5-th percentile240
Q1255
median511
Q3767
95-th percentile1023
Maximum1023
Range783
Interquartile range (IQR)512

Descriptive statistics

Standard deviation298.41289
Coefficient of variation (CV)0.52007658
Kurtosis-1.4150785
Mean573.78643
Median Absolute Deviation (MAD)256
Skewness0.26105279
Sum3.4675578 × 108
Variance89050.252
MonotonicityNot monotonic
2024-12-14T10:48:35.262202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
240 126291
20.9%
255 91437
15.1%
1008 74777
12.4%
496 74388
12.3%
752 72633
12.0%
1023 53893
8.9%
767 53198
8.8%
511 51456
8.5%
766 721
 
0.1%
1022 687
 
0.1%
Other values (24) 4848
 
0.8%
ValueCountFrequency (%)
240 126291
20.9%
241 427
 
0.1%
242 202
 
< 0.1%
243 26
 
< 0.1%
244 4
 
< 0.1%
252 12
 
< 0.1%
253 141
 
< 0.1%
254 670
 
0.1%
255 91437
15.1%
496 74388
12.3%
ValueCountFrequency (%)
1023 53893
8.9%
1022 687
 
0.1%
1021 191
 
< 0.1%
1020 8
 
< 0.1%
1011 20
 
< 0.1%
1010 225
 
< 0.1%
1009 461
 
0.1%
1008 74777
12.4%
767 53198
8.8%
766 721
 
0.1%

V4
Real number (ℝ)

Zeros 

Distinct324
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.96103
Minimum0
Maximum484.488
Zeros84180
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:35.611579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.4875
median3.01875
Q37.48125
95-th percentile100.494
Maximum484.488
Range484.488
Interquartile range (IQR)5.99375

Descriptive statistics

Standard deviation63.269456
Coefficient of variation (CV)3.1696489
Kurtosis27.411076
Mean19.96103
Median Absolute Deviation (MAD)1.53125
Skewness5.1449278
Sum12063029
Variance4003.024
MonotonicityNot monotonic
2024-12-14T10:48:35.949236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.01875 116690
19.3%
1.4875 113858
18.8%
0 84180
13.9%
4.50625 81675
13.5%
5.99375 51380
8.5%
7.48125 31421
 
5.2%
9.0125 13702
 
2.3%
10.5 11483
 
1.9%
11.9875 10288
 
1.7%
17.9812 5691
 
0.9%
Other values (314) 83961
13.9%
ValueCountFrequency (%)
0 84180
13.9%
1.4875 113858
18.8%
3.01875 116690
19.3%
4.50625 81675
13.5%
5.99375 51380
8.5%
7.48125 31421
 
5.2%
9.0125 13702
 
2.3%
10.5 11483
 
1.9%
11.9875 10288
 
1.7%
13.5188 3101
 
0.5%
ValueCountFrequency (%)
484.488 14
 
< 0.1%
483 1
 
< 0.1%
481.512 31
 
< 0.1%
479.981 115
< 0.1%
478.494 123
< 0.1%
477.006 206
< 0.1%
475.475 107
< 0.1%
473.988 54
 
< 0.1%
472.5 42
 
< 0.1%
471.012 12
 
< 0.1%

V5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0
495662 
1
108667 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters604329
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 495662
82.0%
1 108667
 
18.0%

Length

2024-12-14T10:48:36.286058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T10:48:36.517292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 495662
82.0%
1 108667
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 495662
82.0%
1 108667
 
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 495662
82.0%
1 108667
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 495662
82.0%
1 108667
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 495662
82.0%
1 108667
 
18.0%

V6
Real number (ℝ)

High correlation 

Distinct2787
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1715.6884
Minimum0
Maximum4892
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:36.784274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile636
Q11259
median1994
Q32146
95-th percentile2320
Maximum4892
Range4892
Interquartile range (IQR)887

Descriptive statistics

Standard deviation618.17647
Coefficient of variation (CV)0.36030813
Kurtosis-0.78714035
Mean1715.6884
Median Absolute Deviation (MAD)204
Skewness-0.87182661
Sum1.0368402 × 109
Variance382142.15
MonotonicityNot monotonic
2024-12-14T10:48:37.121720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
649 7032
 
1.2%
643 4416
 
0.7%
642 4310
 
0.7%
641 4285
 
0.7%
640 4109
 
0.7%
644 4103
 
0.7%
645 4100
 
0.7%
639 4037
 
0.7%
637 3813
 
0.6%
638 3805
 
0.6%
Other values (2777) 560319
92.7%
ValueCountFrequency (%)
0 5
< 0.1%
91 1
 
< 0.1%
110 2
 
< 0.1%
112 2
 
< 0.1%
128 1
 
< 0.1%
138 1
 
< 0.1%
152 1
 
< 0.1%
443 1
 
< 0.1%
506 1
 
< 0.1%
513 1
 
< 0.1%
ValueCountFrequency (%)
4892 1
< 0.1%
4817 2
< 0.1%
4802 2
< 0.1%
4793 2
< 0.1%
4736 2
< 0.1%
4538 1
< 0.1%
4189 1
< 0.1%
4049 2
< 0.1%
3863 1
< 0.1%
3613 1
< 0.1%

V7
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0
604329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters604329
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 604329
100.0%

Length

2024-12-14T10:48:37.455553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T10:48:37.670900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 604329
100.0%

Most occurring characters

ValueCountFrequency (%)
0 604329
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

V8
Real number (ℝ)

High correlation  Zeros 

Distinct323
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.710354
Minimum0
Maximum82.1
Zeros201832
Zeros (%)33.4%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:38.331253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.8
Q321.9
95-th percentile31.3
Maximum82.1
Range82.1
Interquartile range (IQR)21.9

Descriptive statistics

Standard deviation11.532085
Coefficient of variation (CV)0.90729845
Kurtosis-0.69530306
Mean12.710354
Median Absolute Deviation (MAD)12.2
Skewness0.40671472
Sum7681235.8
Variance132.98898
MonotonicityNot monotonic
2024-12-14T10:48:38.645848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 201832
33.4%
21 4747
 
0.8%
20.2 4499
 
0.7%
19.3 4365
 
0.7%
21.9 4201
 
0.7%
22.6 4193
 
0.7%
19.5 4140
 
0.7%
22.3 4115
 
0.7%
16.5 4109
 
0.7%
21.5 4107
 
0.7%
Other values (313) 364021
60.2%
ValueCountFrequency (%)
0 201832
33.4%
0.2 778
 
0.1%
0.4 703
 
0.1%
0.6 801
 
0.1%
0.8 821
 
0.1%
1 880
 
0.1%
1.3 944
 
0.2%
1.5 964
 
0.2%
1.7 1025
 
0.2%
1.9 1031
 
0.2%
ValueCountFrequency (%)
82.1 2
 
< 0.1%
79.7 1
 
< 0.1%
77.8 1
 
< 0.1%
75.2 1
 
< 0.1%
73.2 5
< 0.1%
72.8 3
< 0.1%
72.6 7
< 0.1%
72.3 4
< 0.1%
72.1 1
 
< 0.1%
71.7 3
< 0.1%

V9
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0
604329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters604329
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 604329
100.0%

Length

2024-12-14T10:48:38.929166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T10:48:39.158327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 604329
100.0%

Most occurring characters

ValueCountFrequency (%)
0 604329
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 604329
100.0%

V10
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
4
445879 
1
127375 
3
 
19722
2
 
9567
7
 
1786

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters604329
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 445879
73.8%
1 127375
 
21.1%
3 19722
 
3.3%
2 9567
 
1.6%
7 1786
 
0.3%

Length

2024-12-14T10:48:39.412247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T10:48:39.652977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
4 445879
73.8%
1 127375
 
21.1%
3 19722
 
3.3%
2 9567
 
1.6%
7 1786
 
0.3%

Most occurring characters

ValueCountFrequency (%)
4 445879
73.8%
1 127375
 
21.1%
3 19722
 
3.3%
2 9567
 
1.6%
7 1786
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 445879
73.8%
1 127375
 
21.1%
3 19722
 
3.3%
2 9567
 
1.6%
7 1786
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 445879
73.8%
1 127375
 
21.1%
3 19722
 
3.3%
2 9567
 
1.6%
7 1786
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 445879
73.8%
1 127375
 
21.1%
3 19722
 
3.3%
2 9567
 
1.6%
7 1786
 
0.3%

V11
Real number (ℝ)

Skewed 

Distinct180939
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.668277
Minimum1.67673
Maximum262.534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-12-14T10:48:39.942163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.67673
5-th percentile5.60198
Q17.94768
median10.7726
Q315.2709
95-th percentile16.95896
Maximum262.534
Range260.85727
Interquartile range (IQR)7.32322

Descriptive statistics

Standard deviation9.9344226
Coefficient of variation (CV)0.85140441
Kurtosis527.15806
Mean11.668277
Median Absolute Deviation (MAD)3.3329
Skewness21.165566
Sum7051478.3
Variance98.692752
MonotonicityNot monotonic
2024-12-14T10:48:40.281939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.5503 33
 
< 0.1%
16.3801 32
 
< 0.1%
16.7474 30
 
< 0.1%
16.2661 30
 
< 0.1%
16.5251 30
 
< 0.1%
16.4008 30
 
< 0.1%
16.3139 30
 
< 0.1%
16.5229 29
 
< 0.1%
16.4636 29
 
< 0.1%
16.3058 29
 
< 0.1%
Other values (180929) 604027
> 99.9%
ValueCountFrequency (%)
1.67673 1
< 0.1%
1.67721 1
< 0.1%
1.67885 1
< 0.1%
1.6791 1
< 0.1%
1.67953 1
< 0.1%
1.67986 1
< 0.1%
1.68001 1
< 0.1%
1.68044 1
< 0.1%
1.68068 1
< 0.1%
1.68105 1
< 0.1%
ValueCountFrequency (%)
262.534 1
< 0.1%
262.522 1
< 0.1%
262.502 1
< 0.1%
262.49 1
< 0.1%
262.463 1
< 0.1%
262.455 1
< 0.1%
262.451 1
< 0.1%
262.447 1
< 0.1%
262.439 1
< 0.1%
262.435 1
< 0.1%

IsAlert
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
1
349785 
0
254544 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters604329
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 349785
57.9%
0 254544
42.1%

Length

2024-12-14T10:48:40.586677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T10:48:40.808109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 349785
57.9%
0 254544
42.1%

Most occurring characters

ValueCountFrequency (%)
1 349785
57.9%
0 254544
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 349785
57.9%
0 254544
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 349785
57.9%
0 254544
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 349785
57.9%
0 254544
42.1%

Interactions

2024-12-14T10:48:01.236758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:44:55.083391image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:02.938787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:11.487196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:20.534352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:28.355527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:39.385083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:46.680868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:55.675720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:03.401957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:12.823112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:20.044527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:29.813275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-12-14T10:46:46.310950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-12-14T10:47:58.779062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:48:07.009739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:00.820390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:09.945623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:18.114201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:26.782752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:36.568206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:45.115041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:53.308403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:01.866883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:10.852510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:18.497160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:28.130742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:35.429767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:44.802420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:51.787872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:00.889914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:08.483948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:17.479273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:24.909016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:34.308361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:42.227899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:50.802803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:59.160625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:48:07.322870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:01.204085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:10.272567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:18.653086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:27.108355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:37.082838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:45.427979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:53.777268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:02.176085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:11.365354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:18.833728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:28.460928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:35.749059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:45.121632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:52.095099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:01.205847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:08.812496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:17.807790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:25.382524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:34.650473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:42.715439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:51.132358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:59.697337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:48:07.657257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:01.649844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:10.585914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:19.145865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:27.414135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:38.009756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:45.758319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:54.199181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:02.481775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:11.844260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:19.138681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:28.793173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:36.045822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:45.417426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:52.401317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:01.509609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:09.132871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:18.131909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:25.842483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:34.969323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:43.173873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:51.450255image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:48:00.198251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:48:07.973928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:02.057313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:10.877439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:19.626272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:27.727633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:38.442962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:46.074135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:54.690313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:02.803745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:12.151947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:19.438867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:29.109200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:36.366497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:45.711916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:52.727091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:01.846415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:09.489399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:18.461312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:26.304303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:35.297018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:43.656369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:51.754542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:48:00.631326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:48:08.285532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:02.464818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:11.187979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:20.081768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:28.047768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:38.899384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:46.379012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:45:55.142103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:03.109286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:12.489487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:19.741101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:29.461014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:36.675676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:46.011433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:46:53.029037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:02.150839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:09.924958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:18.763385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:26.753509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:35.642868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:44.094834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:47:52.054827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-14T10:48:00.910928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-14T10:48:41.027262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
E1E10E11E2E3E4E5E6E7E8E9IsAlertP1P2P3P4P5P6P7V1V10V11V2V3V4V5V6V8
E11.0000.119-0.0530.8870.152-0.0170.0230.0120.1060.0560.0610.165-0.035-0.0340.010-0.010-0.1290.089-0.0890.2680.203-0.0720.0480.019-0.1170.1290.2460.118
E100.1191.000-0.0210.0020.161-0.020-0.0130.0030.2630.3270.9240.4130.059-0.008-0.0100.010-0.001-0.1740.1740.3860.2040.3750.0980.033-0.1130.2790.3730.236
E11-0.053-0.0211.000-0.0370.032-0.0190.0130.019-0.041-0.0180.0310.0810.0450.0020.009-0.009-0.013-0.0910.091-0.2490.1470.0910.0400.0240.1280.117-0.281-0.318
E20.8870.002-0.0371.0000.200-0.0120.0300.0280.033-0.0000.1120.156-0.039-0.0310.014-0.014-0.1240.103-0.1030.0710.106-0.1340.030-0.006-0.0680.1100.0630.028
E30.1520.1610.0320.2001.0000.0350.1210.2440.0670.1140.0320.1760.0550.0900.0280.0560.0140.0030.2650.1500.0460.0080.0600.0370.1460.0800.0780.056
E4-0.017-0.020-0.019-0.0120.0351.0000.099-0.007-0.092-0.0450.2580.163-0.0000.002-0.0060.006-0.004-0.0180.018-0.0260.123-0.0080.012-0.0170.0270.087-0.032-0.016
E50.023-0.0130.0130.0300.1210.0991.000-0.0100.0180.0290.1250.148-0.010-0.0090.003-0.003-0.0250.001-0.0010.0360.139-0.0120.0150.002-0.0930.1920.0360.054
E60.0120.0030.0190.0280.244-0.007-0.0101.0000.034-0.0730.3810.193-0.024-0.012-0.0070.007-0.0080.031-0.031-0.0670.153-0.001-0.058-0.0250.0550.111-0.059-0.082
E70.1060.263-0.0410.0330.067-0.0920.0180.0341.0000.5110.8060.361-0.013-0.001-0.0060.006-0.0740.012-0.0120.3210.068-0.0000.0710.044-0.1230.0690.3060.202
E80.0560.327-0.018-0.0000.114-0.0450.029-0.0730.5111.0000.7150.3890.0110.002-0.0130.013-0.082-0.0850.0850.2410.2380.0430.0970.044-0.0590.1960.2290.164
E90.0610.9240.0310.1120.0320.2580.1250.3810.8060.7151.0000.3800.0270.0070.0210.0200.0060.0040.0820.1590.1180.0260.0330.0320.0690.0630.1300.097
IsAlert0.1650.4130.0810.1560.1760.1630.1480.1930.3610.3890.3801.0000.0500.0490.0150.0450.0210.0090.2490.2840.2740.0260.0440.0680.1430.0550.2660.199
P1-0.0350.0590.045-0.0390.055-0.000-0.010-0.024-0.0130.0110.0270.0501.0000.013-0.0070.0070.059-0.1770.177-0.0290.1440.259-0.0130.0140.0300.120-0.017-0.032
P2-0.034-0.0080.002-0.0310.0900.002-0.009-0.012-0.0010.0020.0070.0490.0131.000-0.0020.0020.044-0.0880.088-0.0080.0440.0390.0110.007-0.0150.041-0.0070.009
P30.010-0.0100.0090.0140.028-0.0060.003-0.007-0.006-0.0130.0210.015-0.007-0.0021.000-1.0000.1100.008-0.008-0.0080.013-0.0100.005-0.0090.0080.015-0.008-0.006
P4-0.0100.010-0.009-0.0140.0560.006-0.0030.0070.0060.0130.0200.0450.0070.002-1.0001.000-0.110-0.0080.0080.0080.0120.010-0.0050.009-0.0080.0190.0080.006
P5-0.129-0.001-0.013-0.1240.014-0.004-0.025-0.008-0.074-0.0820.0060.0210.0590.0440.110-0.1101.0000.044-0.044-0.0460.0360.315-0.037-0.0170.0720.007-0.043-0.020
P60.089-0.174-0.0910.1030.003-0.0180.0010.0310.012-0.0850.0040.009-0.177-0.0880.008-0.0080.0441.000-1.0000.0620.059-0.416-0.018-0.032-0.0190.0230.0320.012
P7-0.0890.1740.091-0.1030.2650.018-0.001-0.031-0.0120.0850.0820.2490.1770.088-0.0080.008-0.044-1.0001.000-0.0620.1420.4160.0180.0320.0190.119-0.032-0.012
V10.2680.386-0.2490.0710.150-0.0260.036-0.0670.3210.2410.1590.284-0.029-0.008-0.0080.008-0.0460.062-0.0621.0000.604-0.0350.1740.097-0.3620.3230.9210.528
V100.2030.2040.1470.1060.0460.1230.1390.1530.0680.2380.1180.2740.1440.0440.0130.0120.0360.0590.1420.6041.0000.0960.1410.1160.3280.3080.4720.302
V11-0.0720.3750.091-0.1340.008-0.008-0.012-0.001-0.0000.0430.0260.0260.2590.039-0.0100.0100.315-0.4160.416-0.0350.0961.000-0.0920.0250.1130.073-0.029-0.058
V20.0480.0980.0400.0300.0600.0120.015-0.0580.0710.0970.0330.044-0.0130.0110.005-0.005-0.037-0.0180.0180.1740.141-0.0921.000-0.1120.1450.3570.1740.162
V30.0190.0330.024-0.0060.037-0.0170.002-0.0250.0440.0440.0320.0680.0140.007-0.0090.009-0.017-0.0320.0320.0970.1160.025-0.1121.000-0.0230.0530.0980.096
V4-0.117-0.1130.128-0.0680.1460.027-0.0930.055-0.123-0.0590.0690.1430.030-0.0150.008-0.0080.072-0.0190.019-0.3620.3280.1130.145-0.0231.0000.287-0.324-0.216
V50.1290.2790.1170.1100.0800.0870.1920.1110.0690.1960.0630.0550.1200.0410.0150.0190.0070.0230.1190.3230.3080.0730.3570.0530.2871.0000.2970.189
V60.2460.373-0.2810.0630.078-0.0320.036-0.0590.3060.2290.1300.266-0.017-0.007-0.0080.008-0.0430.032-0.0320.9210.472-0.0290.1740.098-0.3240.2971.0000.608
V80.1180.236-0.3180.0280.056-0.0160.054-0.0820.2020.1640.0970.199-0.0320.009-0.0060.006-0.0200.012-0.0120.5280.302-0.0580.1620.096-0.2160.1890.6081.000

Missing values

2024-12-14T10:48:08.717736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-14T10:48:11.129245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

P1P2P3P4P5P6P7P8E1E2E3E4E5E6E7E8E9E10E11V1V2V3V4V5V6V7V8V9V10V11IsAlert
034.74069.84593140042.85710.290601572104.89500.00.01-200.015875324111570.0101.960.1757525.9937502005013.40414.80040
134.421513.41120140042.85710.290601572104.89500.00.01-200.015875324111570.0101.980.4557525.9937502007013.40414.77290
234.344715.18520140042.85710.290601576104.16700.00.01-200.015875324111570.0101.970.2807525.9937502011013.40414.77360
334.34218.84696140042.85710.290601576104.16700.00.01-200.015875324111570.0101.990.0707525.9937502015013.40414.76670
434.332214.69940140042.85710.290601576104.16700.00.01-200.015875324111570.0102.070.1757525.9937502017013.40414.77570
534.372913.64440140042.85710.290601576104.16700.00.01-200.015875324111570.0102.000.2807525.9937502016013.40414.74980
634.385110.16540140042.85710.290601576104.16700.00.01-200.015875324111570.0102.010.1757525.9937502016013.40414.75780
734.431313.32700140042.85710.290601576104.16700.00.01-200.015875324111570.0101.930.2807525.9937502011013.40414.77290
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Duplicate rows

Most frequently occurring

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